检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

3,226 篇 会议
56 册 图书
26 篇 期刊文献

馆藏范围

3,308 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

1,734 篇 工学
- 1,335 篇 计算机科学与技术...
- 1,225 篇 软件工程
- 212 篇 控制科学与工程
- 206 篇 信息与通信工程
- 197 篇 电气工程
- 153 篇 机械工程
- 136 篇 材料科学与工程（可...
- 132 篇 安全科学与工程
- 128 篇 生物工程
- 126 篇 生物医学工程（可授...
- 94 篇 土木工程
- 93 篇 交通运输工程
- 85 篇 建筑学
- 79 篇 动力工程及工程热...
- 75 篇 电子科学与技术（可...
- 70 篇 化学工程与技术
- 62 篇 力学（可授工学、理...
710 篇 理学
- 498 篇 数学
- 174 篇 统计学（可授理学、...
- 132 篇 生物学
- 115 篇 物理学
- 113 篇 系统科学
- 60 篇 化学
392 篇 管理学
- 283 篇 管理科学与工程(可...
- 172 篇 图书情报与档案管...
- 129 篇 工商管理
94 篇 医学
- 86 篇 基础医学(可授医学...
- 81 篇 临床医学
56 篇 经济学
- 56 篇 应用经济学
51 篇 教育学
- 50 篇 教育学
43 篇 法学
32 篇 农学
6 篇 文学
5 篇 军事学
1 篇 艺术学

主题

947 篇 predictive model...
267 篇 accuracy
259 篇 data models
235 篇 computational mo...
203 篇 analytical model...
180 篇 prediction algor...
148 篇 training
138 篇 software enginee...
130 篇 feature extracti...
120 篇 software
118 篇 machine learning
116 篇 deep learning
104 篇 neural networks
103 篇 forecasting
97 篇 adaptation model...
83 篇 mathematical mod...
82 篇 machine learning...
76 篇 optimization
70 篇 real-time system...
62 篇 support vector m...

机构

10 篇 school of inform...
7 篇 national enginee...
7 篇 hubei key labora...
7 篇 school of electr...
7 篇 school of automa...
6 篇 school of comput...
6 篇 school of softwa...
6 篇 school of electr...
6 篇 engineering rese...
5 篇 school of comput...
5 篇 school of transp...
5 篇 nanjing univ sta...
5 篇 chitkara univers...
5 篇 school of softwa...
4 篇 school of inform...
4 篇 naval university...
4 篇 school of manage...
4 篇 school of softwa...
4 篇 software enginee...
4 篇 school of electr...

作者

8 篇 labiche yvan
7 篇 hassan ahmed e.
7 篇 menzies tim
5 篇 yang yang
5 篇 mcintosh shane
5 篇 chen li
5 篇 ziadi tewfik
5 篇 yan wang
4 篇 yu wang
4 篇 ming li
4 篇 liu yang
4 篇 hall tracy
4 篇 xintian liu
4 篇 bowes david
4 篇 monden akito
4 篇 min wu
4 篇 matsumoto ken-ic...
4 篇 yu zhang
4 篇 lei zhang
4 篇 briand lionel c.

语言

3,159 篇 英文
138 篇 其他
23 篇 中文
1 篇 俄文

检索条件"任意字段=Proceedings of the 6th International Conference on Predictive Models in Software Engineering"

共 3308 条记录，以下是781-790 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

predictive Model for Metastasis Timing in Triple-Negative Breast Cancer

Predictive Model for Metastasis Timing in Triple-Negative Br...

引用

international conference on Computational Intelligence and Networks (CINE)

作者： Padmashree Desai C Sujatha Vaishak S Kolhar Namita V Bhat G Shrishail Sanjana Patil School of Computer Science and Engineering KLE Technological University Hubli Karnataka

ISBN: (数字)9798331516796

ISBN: (纸本)9798331516802

the Proposed work focuses on implementing a machine learning approach to predict the period required for diagnosing patients with metastatic triple-negative breast cancer(TNBC). the main goal is to accurately predict the diagnostic period of metastatic TNBC using the various attributes in the dataset provided by Gilead Sciences in association with women in data science (WiDS) challenge #2, WiDS Datathon 2024: Equity in Healthcare, where KLE Technological University is part of WiDS datathon challenge #2 the provided raw dataset was cleaned and preprocessed using the standard exploratory data analysis (EDA) approach. the method implemented for the predictive model building is *** model achieved an accuracy of Root Mean Square Error (RMSE) of 82.94%. We tried our level best to apply. this research is associated with the sustainable development goal (SDG) 3: Ensure Healthy Lives and Promote Well-Being for all and mapping to a target 3.4. Reduce mortality from non-communicable diseases and promote mental health

关键词： Temperature distribution Accuracy Pollution Mortality predictive models Timing Metastasis Sustainable development Root mean square Standards

来源：评论

学校读者我要写书评

暂无评论

Digital Twins of Production Systems Based on Discrete Simulation and Machine Learning Algorithms 1

引用

proceedings of the 18th international conference on Soft Computing models in Industrial and Environmental Applications, SOCO 2023

作者： Krenczyk, Damian Faculty of Mechanical Engineering Silesian University of Technology Gliwice Poland

ISBN: (数字)9783031425363

ISBN: (纸本)9783031425356

the implementation of the concept of a digital twin of a production system based on discrete-event simulation models, apart from the possibility of simulating the features, behavior, functioning and performance of a real counterpart, requires supplementing the model with analytical solutions. Such a solution will allow the digital twin to be included in the decision support process in the area of production management, and the obtained analysis results can be sent back to the executive elements of the production system or planning systems. the paper presents the concept and practical verification of the possibility of creating digital twins of production systems based on the integration of modern discrete simulation software with machine learning modules - reinforcement learning. this combination also gives the opportunity to effectively conduct optimization and adaptation activities and increase the level of autonomy and flexibility of production systems. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Discrete event simulation

来源：评论

学校读者我要写书评

暂无评论

Forecasting Electricity Consumption in Five Regions of Dhaka City: A Comparative Assessment among various ML-Boosting models

Forecasting Electricity Consumption in Five Regions of Dhaka...

引用

international conference on Electrical engineering and Information & Communication Technology (ICEEICT)

作者： Md. Mehedi Hasan Muaz M. Akhtaruzzaman Md. Mahbubur Rahman Department of Computer Science and Engineering Military Institute of Science and Technology (MIST) Dhaka Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

Predicting electricity usage is vital to maintain a consistent power supply, making it a top concern for all nations. However, in Bangladesh, research in this area is inadequate. So, this research focuses on predicting power consumption in some regions of Dhaka City using XGBoost, LightGBM, and CatBoost algorithms. Monthly consumption, sanctioned load, meter type, and tariff are the key factors employed in the prediction process where the grid search technique is utilized to discover the most effective hyper-parameters. Model performance is evaluated by MAE, RMSE, and R2 scores. Experimental results indicate that CatBoost outperforms other algorithms (RMSE 2.0% to 42.0 % ), although LightGBM offers faster training. Implementing these techniques holds significant promise for enhancing power infrastructure planning and energy forecasting, ultimately minimizing system losses and equipment damages.

关键词： Training Power demand Electricity Urban areas Tariffs predictive models Prediction algorithms

来源：评论

学校读者我要写书评

暂无评论

Driving Data Analysis for the Development of Kuala Terengganu Driving Cycle 6th

Driving Data Analysis for the Development of Kuala Terenggan...

引用

6th international conference on Mechanical engineering Research, ICMER 2021

作者： Anida, I.N. Norbakyah, J.S. Yussof, W.N.J.H.W. Walker, P. Salisa, A.R. Faculty of Ocean Engineering Technology and Informatics Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia Universiti Malaysia Terengganu Terengganu Kuala Nerus21030 Malaysia School of Mechanical and Mechatronic Engineering Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia

ISBN: (纸本)9789811915765

Driving cycle plays a vital role in the production and evaluating the performance of the vehicle. Driving cycle is a speed-time data set and as an important input for vehicle emission models. A problem coming with the second-by-second speed driving data is to analyze the big driving data. To analyze this big data, it is necessary to choose best big data analysis methods, which give opportunity to store, preprocess, detect outlier and apply classification or clustering algorithms. In this study, a set of driving data is stored, managed and analyzed using Tall Arrays (TA) and k-means clustering algorithms in MATLAB for the development of Kuala Terengganu Driving Cycle (KTDC). the objectives of this paper are;to store and manage driving data using TA in MATLAB, to develop a KTDC by using k-means clustering, and lastly to analyze the energy consumption and emissions of KTDC. Firstly, the driving data is collected in five different routes in Kuala Terengganu city at go-to-work times. then the data is stored and analyzed in the MATLAB. the development of KTDC is by using k-means clustering approach. Finally, the energy consumption and emissions of KTDC is analyzed by using AUTONOMIE software. KTDC is successfully developed with 35.15 km/h in average speed and 12 micro-trips. © 2023, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MATLAB

来源：评论

学校读者我要写书评

暂无评论

Inversion Research of Horizontal Well Array Induction Logging Based on Neural Network

Inversion Research of Horizontal Well Array Induction Loggin...

引用

Intelligent Control, Measurement and Signal Processing and Intelligent Oil Field (ICMSP), international conference on

作者： Jie Wu Lirong Tian School of Electronic Engineering Xi'an Shiyou University Xi 'an China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

In order to improve the computational efficiency and accuracy of the nonlinear inversion of horizontal well array induction logging, the neural network is introduced into the inversion of induction logging data. Firstly, the database of horizontal well array induction logging samples is established. then, BP, RBF and convolutional neural network prediction models are built respectively, and training and testing are carried out based on sample database. During training, log response is taken as the input of neural network, and formation parameters are taken as the output of neural network. the prediction results of different neural network models are compared and analyzed, and the prediction results are visualized by graphs. the results show that the relative error between the inversion results of the formation parameters and the true value of the formation model is lower than 5%, and the evaluation indexes and prediction accuracy of the convolutional neural network prediction model are better than those of the BP and RBF neural network prediction models.

关键词： Training Accuracy Databases Neural networks predictive models Conductivity Data models Numerical models Arrays Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Resilient predictive Control of Constrained Connected and Automated Vehicles under Malicious Attacks

Resilient Predictive Control of Constrained Connected and Au...

引用

Industrial Cyber-Physical Systems (ICPS)

作者： Henglai Wei Yan Wang Jicheng Chen Hui Zhang Centre of Excellence for Testing Research of Autonomous Vehicles NTU (CETRAN) and School of Mechanical and Aerospace Engineering Nangyang Technological University Singapore School of Mechanical and Aerospace Engineering Nangyang Technological University Singapore School of Transportation Science and Engineering Beihang University Beijing China

In this paper, we present a novel resilient distributed model predictive control (RDMPC) framework for the con-strained Connected and Automated Vehicles (CAV) in the pres-ence of $F$ -local malicious attacks. the proposed framework aims to ensure constraint satisfaction and identify malicious attacks using previously broadcast information and a convex set, referred to as the ”resilience set.“ Compared to the well-known Mean Subsequence Reduced (MSR) algorithms that require (2F + 1)-robust graphs, the proposed approach significantly reduces the required robustness level to (F + 1)-robust graph. Our simulation results demonstrate the effectiveness of the proposed approach in mitigating the impact of malicious attacks on constrained CAVs while ensuring constraint satisfaction. Overall, the proposed RDMPC framework contributes to the field of resilient platoon control for CAVs and has potential implications for improving the reliability and security of CAVs in real-world scenarios.

关键词： Uncertainty Simulation predictive models Cyber-physical systems Robustness Security Communication networks

来源：评论

学校读者我要写书评

暂无评论

Multi-Criteria Optimization for Photovoltaic Power Forecasting based on CNN-BiGRU

Multi-Criteria Optimization for Photovoltaic Power Forecasti...

引用

international conference on Electronic engineering and Informatics (EEI)

作者： Wenshuai Lin Peitao Gao Zilong Zhang Chun Shan School of Electronics and Information Guangdong Polytechnic Normal University Guangzhou China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Accurate and stable prediction of PV generation has the potential to significantly enhance the stability of power systems and mitigate the reliance on conventional environmental resources. Nevertheless, many prediction models lack data processing and do not consider the model's generalisation capacity, resulting in poor robustness of the model. To solve this gap, a hybrid deep learning framework based on multi-criteria optimization and successive variational modal decomposition is presented. Firstly, the Pearson correlation analysis thermodynamic diagram is utilised to determine the input characteristics of the model, then the PV data undergoes a process of consecutive variational modal decomposition to mitigate the presence of high-frequency noise. Furthermore, a Bidirectional Gated Recurrent Unit (BiGRU) network is employed to further investigate the temporal dependencies within each subsequence. Meantime, multi-criteria particle swarm optimization is utilized to optimize the important parameter of BiGRU model. the experimental findings demonstrate that the prediction model has superior prediction accuracy compared to alternative models.

关键词： Photovoltaic systems Analytical models Accuracy predictive models Power system stability Feature extraction Prediction algorithms Data models Stability analysis Optimization

来源：评论

学校读者我要写书评

暂无评论

Beam Prediction in 5G and 6G Systems: A Deep Learning Approach

Beam Prediction in 5G and 6G Systems: A Deep Learning Approa...

引用

international conference on Communications, Signal Processing, and their Applications (ICCSPA)

作者： Shaimaa Hassanein Omar Shouman Elias Yaacoub Abdulaziz Al-Ali Tamer Khattab Computer Science and Engineering Qatar University Doha Qatar IIPL Lab Electrical Engineering Qatar University Doha Qatar

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

Exploiting beamforming techniques to direct radio waves into narrow beams targeted at users are essential in 5G and 6G to ensure high data rate requirements with low latency. However, beamforming in a dynamic environment is challenging due to the high sensitivity to shift in the receiver location. therefore, an adaptive beamforming approach is needed. In this paper, we utilize DeepSense 6G and 5G MIMO datasets for optimal beam prediction using deep learning approaches. We introduce a simple approach to transform radar input into vector embeddings in the DeepSense 6G dataset. Moreover, we address the severe minority classes problem in the 5G MIMO dataset. Finally, we conduct a thorough analysis of the state-of-the-art models. the proposed model shows significant improvements in the Top-1, Top-3, Top-5, weighted average and distance based accuracies.

关键词： 6G mobile communication Deep learning Accuracy 5G mobile communication Array signal processing Time series analysis Transforms predictive models Data models Vectors

来源：评论

学校读者我要写书评

暂无评论

Towards Holistic Crime Management through Descriptive Analysis and predictive Forecasting

Towards Holistic Crime Management Through Descriptive Analys...

引用

Advancements in Computing (ICAC), international conference on

作者： Tinaya Wijethunge Sampath Edirisinghe Mansik Jeon Jeehyun Kim Ruchire Wijesinghe Bhagya Silva Department of Computer Engineering Faculty of Engineering Nugegoda Sri Lanka Daegu Republic of Korea Department of Electrical and Electronic Engineering Faculty of Engineering Malabe Sri Lanka Center for Excellence in Informatics Electronics Transmission (CIET) Malabe Sri Lanka Department of Information Technology Faculty of Computing Malabe Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

throughout the evolution of humankind, devising effective crime prevention strategies has been a concern in fostering safer communities. Although achieving a zero-crime rate is not possible yet, using data management methods can contribute to preventing and minimizing crimes. this article presents an integrated approach to proactive crime prevention. Data insights derived from analysis and visualization provide a comprehensive overview of historical trends and scenarios, aiding understanding of past and present situations. the forecasting of crime rates extends this comprehension to the future, spotlighting the severity of the potential threat to the safety of the community. three distinct prediction models are presented to predict the occurrence of violent/non-violent crimes, the most probable crime type, and the most probable crime location in a spatial-temporal context, facilitating optimal resource allocation of police patrolling. Combining these three analytical components, an application is designed to present this holistic solution for effective crime prevention, with a 73% accuracy in predicting violent crimes. To achieve higher accuracy in predicting the most probable crime types and locations, it is essential to go beyond spatiotemporal context and augment data on other factors that affect the occurrence of crimes.

关键词： Adaptation models Accuracy Uncertainty Prevention and mitigation Computational modeling predictive models Data models Spatiotemporal phenomena Forecasting Meteorology

来源：评论

学校读者我要写书评

暂无评论

Bluetooth Mesh Routing Optimization Based on DDPG

Bluetooth Mesh Routing Optimization Based on DDPG

引用

6th international conference on Intelligent Computing and Signal Processing (ICSP)

作者： Zhiqin Liu School of Electronics and Information Engineering Shanghai University of Electric Power Shanghai China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

this article carries out multi-objective optimization for the packet loss rate and delay of the Bluetooth Mesh network. Apply GRU-based network flow prediction and DDPG's intelligent routing method, and obtain the Bluetooth Mesh network load information under the SDN architecture to construct a Mesh network including delay, using the original traffic matrix and the predicted traffic matrix as the state space, based on deep determinism Policy Gradient (DDPG) algorithm simulation to obtain optimal routing, effectively allocate routing resources and manage network traffic. Experimental results show that compared with before optimization, the routes generated by this algorithm can reduce the packet loss rate and delay of the Bluetooth Mesh network.

关键词： Mesh networks Bluetooth Signal processing algorithms Packet loss Telecommunication traffic predictive models Prediction algorithms Routing Delays Optimization

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共331页 << < 75 76 77 78 79 80 81 82 83 84 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：